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protein structure prediction:Challenges,Advances,and the Shift of Research Paradigms

Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： protein folding protein structure prediction Deep learning Transformer Language model

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protein structure prediction with energy minimization and deep learning approaches

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NATURAL COMPUTING 2023年第4期22卷 659-670页

作者： Filgueiras, Juan Luis Varela, Daniel Santos, Jose Univ A Coruna CITIC Ctr Informat & Commun Technol Res Dept Comp Sci & Informat Technol La Coruna Spain

In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are discussed. On the other hand, methods based on protein conformational energy minimization and with different search strategies are analyzed. In this latter case, our methods based on a memetic combination between differential evolution and the fragment replacement technique are included, incorporating also the possibility of niching in the evolutionary search. Different proteins have been used to analyze the pros and cons in both approaches, proposing possibilities of integration of both alternatives.

关键词： protein structure prediction Differential evolution Evolutionary computing niching methods Crowding niching method Deep learning

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protein structure prediction using the evolutionary algorithm USPEX

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proteinS-structure FUNCTION AND BIOINFORMATICS 2023年第7期91卷 933-943页

作者： Rachitskii, Pavel Kruglov, Ivan Finkelstein, Alexei V. Oganov, Artem R. Moscow Inst Phys & Technol Dolgoprudnyi Russia Dukhov Res Inst Automatics VNIIA Moscow Russia Russian Acad Sci Inst Prot Res Moscow Russia Lomonosov Moscow State Univ Biol Dept Moscow Russia Lomonosov Moscow State Univ Biotechnol Dept Moscow Russia Skolkovo Inst Sci & Technol Skolkovo Innovat Ctr Moscow Russia Moscow Inst Phys & Technol Dolgoprudnyi 141700 Russia

protein structure prediction is one of major problems of modern biophysics: current attempts to predict the tertiary protein structure from amino acid sequence are successful mostly when the use of big data and machine learning allows one to reduce the "prediction problem" to the "problem of recognition". Compared with recent successes of deep learning, classical predictive methods lag behind in their accuracy for the prediction of stable conformations. Therefore, in this work we extended the evolutionary algorithm USPEX to predict protein structure based on global optimization starting with the amino acid sequence. Moreover, we compared frequently used force fields for the task of protein structure prediction. protein structure relaxation and energy calculations were performed using Tinker (with several different force fields) and Rosetta (with REF2015 force field) codes. To create new protein structure models in the USPEX algorithm, we developed novel variation operators. The test of the new method on seven proteins having (for simplicity) no cis-proline (with omega approximate to 0 degrees) residues, and a length of up to 100 residues, revealed that our algorithm predicts tertiary structures of proteins with high accuracy. The comparison of the final potential energies of the predicted protein structures obtained using the USPEX and the Rosetta Abinitio approach showed that in most cases the developed algorithm found structures with close or even lower energy (Amber/Charmm/Oplsaal) and scoring function (REF2015). While USPEX has clearly demonstrated its ability to find very deep energy minima, our study showed that the existing force fields are not sufficiently accurate for accurate blind prediction of protein structures without further experimental verification.

关键词： evolutionary algorithm protein folding protein structure prediction USPEX variation operator

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protein structure prediction in an atomic model with differential evolution integrated with the crowding niching method

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NATURAL COMPUTING 2022年第4期21卷 537-551页

作者： Varela, Daniel Santos, Jose Univ A Coruna Dept Comp Sci & Informat Technol CITIC Ctr Informat & Commun Technol Res La Coruna Spain

A hybrid version between differential evolution and the fragment replacement technique was defined for protein structure prediction. The coarse-grained atomic model of the Rosetta system was used for protein representation. The high-dimensional and multimodal nature of protein energy landscapes requires an efficient search for obtaining the native structures with minimum energy. However, the energy model of Rosetta presents an additional difficulty, since the best energy area in the landscape does not necessarily correspond to the closest conformations to the native structure. A strategy is to obtain a diverse set of protein conformations that correspond to different minima in the landscape. The incorporation of the crowding niching method into the hybrid evolutionary algorithm allows addressing the problem of the energy landscape deceptiveness, allowing to obtain a set of optimized and diverse protein folds.

关键词： protein structure prediction Differential evolution Evolutionary computing niching methods Crowding niching method

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protein structure prediction with Mass Spectrometry Data

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ANNUAL REVIEW OF PHYSICAL CHEMISTRY 2022年第1期73卷 1-19页

作者： Biehn, Sarah E. Lindert, Steffen Ohio State Univ Dept Chem & Biochem Columbus OH 43210 USA

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein foot-printing, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data.

关键词： computational methods mass spectrometry protein structure prediction sparse experimental data

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protein structure prediction improves the quality of amino-acid sequence alignment

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proteinS-structure FUNCTION AND BIOINFORMATICS 2022年第12期90卷 2144-2147页

作者： Lesk, Arthur M. Konagurthu, Arun S. Penn State Univ Dept Biochem & Mol Biol University Pk PA 16802 USA Monash Univ Dept Data Sci & Artificial Intelligence Clayton Vic Australia

The basic operation in analysis of protein evolution is alignment: the specification of residue-residue correspondences. A structural alignment is a specification of residue-residue correspondences based on the atomic positions in the structures of two or more proteins. It is well-known that structural alignments are more accurate, over a much wider range of divergence, than pairwise alignments based solely on sequences-for instance computed with the Needleman-Wunsch algorithm with affine gap penalties. Given the amino-acid sequences of two proteins, alignments based solely on the sequences fall into "daylight", "twilight", and "midnight" zones, in which the fidelity of the correspondences diminishes in accuracy, and in strength of ability to distinguish true homology from noise. The success of AlphaFold2 in template-free modeling of three-dimensional structures from one-dimensional amino-acid sequence information implies that: given the amino-acid sequences of two or more proteins, in the absence of experimentally determined structures, reliable alignments-even for very highly diverged proteins-could in many cases be achieved by applying AlphaFold2 to the sequences, and performing structural alignments of the models.

关键词： AlphaFold2 amino-acid sequence alignment CASP14 protein structure prediction

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LightRoseTTA: High-Efficient and Accurate protein structure prediction Using a Light-Weight Deep Graph Model

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ADVANCED SCIENCE 2025年第19期12卷 e2309051页

作者： Wang, Xudong Zhang, Tong Liu, Guangbu Cui, Zhen Zeng, Zhiyong Long, Cheng Zheng, Wenming Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Nanyang Technol Univ Sch Comp Engn 50 Nanyang Ave Singapore 639798 Singapore Southeast Univ Sch Biol Sci & Med Engn Nanjing 210096 Peoples R China

Accurately predicting protein structure, from sequences to 3D structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, is proposed with promising success. Here, "a light-weight deep graph network, named LightRoseTTA," is reported to achieve accurate and highly efficient prediction for proteins. Notably, three highlights are possessed by LightRoseTTA: i) high-accurate structure prediction for proteins, being "competitive with RoseTTAFold" on multiple popular datasets including CASP14 and CAMEO;ii) high-efficient training and inference with a light-weight model, costing "only 1 week on one single NVIDIA 3090 GPU for model-training" (vs 30 days on 8 NVIDIA V100 GPUs for RoseTTAFold) and containing "only 1.4M parameters" (vs 130M in RoseTTAFold);iii) low dependency on multi-sequence alignment (MSA), achieving the best performance on three MSA-insufficient datasets: Orphan, De novo, and Orphan25. Besides, LightRoseTTA is "transferable" from general proteins to antibody data, as verified in the experiments. The time and resource costs of LightRoseTTA and RoseTTAFold are further discussed to demonstrate the feasibility of light-weight models for protein structure prediction, which may be crucial in resource-limited research for universities and academic institutions. The code and model are released to speed biological research ().

关键词： graph neural network light-weight deep learning model protein structure prediction

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proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction

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CURRENT OPINION IN STRUCTURAL BIOLOGY 2025年 90卷 102973页

作者： Chakravarty, Devlina Lee, Myeongsang Porter, Lauren L. Natl Ctr Biotechnol Informat Natl Lib Med NIH Bethesda MD 20894 USA NHLBI Biochem & Biophys Ctr NIH Bethesda MD 20892 USA

In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training- set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high- confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably.

关键词： Machine learning Deep learning Alternative conformations Fold- switching proteins protein structure prediction Metamorphic proteins

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Artificial intelligence for template-free protein structure prediction: a comprehensive review

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第8期56卷 7665-7732页

作者： Mufassirin, M. M. Mohamed Newton, M. A. Hakim Sattar, Abdul Griffith Univ Sch ICT 170 Kessels RdNathan Brisbane Qld 4111 Australia Griffith Univ IIIS 170 Kessels RdNathan Brisbane Qld 4111 Australia South Eastern Univ Sri Lanka Dept Comp Sci Main StSammanthurai Ampara 32200 Eastern Provinc Sri Lanka Univ Newcastle Sch Informat & Phys Sci Univ Dr Callaghan NSW 2308 Australia

protein structure prediction (PSP) is a grand challenge in bioinformatics, drug discovery, and related fields. PSP is computationally challenging because of an astronomically large conformational space to be searched and an unknown very complex energy function to be minimised. To obtain a given protein's structure, template-based PSP approaches adopt a similar protein's known structure, while template-free PSP approaches work when no similar protein's structure is known. Currently, proteins with known structures are greatly outnumbered by proteins with unknown structures. Template-free PSP has obtained significant progress recently via machine learning and search-based optimisation approaches. However, very accurate structures for complex proteins are yet to be achieved at a level suitable for effective drug design. Moreover, ab initio prediction of a protein's structure only from its amino acid sequence remains unsolved. Furthermore, the number of protein sequences with unknown structures is growing rapidly. Hence, to make further progress in PSP, more sophisticated and advanced artificial intelligence (AI) approaches are needed. However, getting involved in PSP research is difficult for AI researchers because of the lack of a comprehensive understanding of the whole problem, along with the background and the literature of all related sub-problems. Unfortunately, existing PSP review papers cover PSP research at a very high level and only some parts of PSP and only from a particular singular viewpoint. Using a systematic approach, this review paper provides a comprehensive survey of the state-of-the-art template-free PSP research to fill this knowledge gap. Moreover, covering required PSP preliminaries and computational formulations, this paper presents PSP research from AI perspectives, discusses the challenges, provides our commentaries, and outlines future research directions.

关键词： Bioinformatics protein structure prediction Machine learning Deep learning Search-based optimisation

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Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction

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JOURNAL OF BIOMOLECULAR structure & DYNAMICS 2025年第6期43卷 2901-2916页

作者： Jadhav, Swati Vyavahare, Arati J. Sharma, Manish DY Patil Coll Engn Elect & Telecommun Dept Pune Maharashtra India PESs Modern Coll Engn Elect & Telecommun Dept Pune Maharashtra India

protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary structure prediction (PSSP) is the foundation for three-dimensional PSP. An Advanced hybrid ensemble deep predictor is utilized for predicting the structure of a protein using Long-Short Term Memory (LSTM), in which the performance of the predictor is improved for obtaining the features through the Salp-J Colony Optimization, which is developed by integrating the features of three optimizations the exploration behavior of Ulmaris, the immune system of virus colony and the teamwork of salp for solution update that helps to predict the accurate protein structure. The proposed method achieved the value of 99.1% accuracy, 99.5% sensitivity, 98.85% specificity, and 0.9% error at the 80% of training percentage 90 using CullPDB. Similarly, in protein Net, the attained value of accuracy is 97.27%, sensitivity is 98.13%, specificity is 97%, and error is 2.7% concerning training percentage 90%.Communicated by Ramaswamy H. Sarma

关键词： protein structure prediction deep predictor long short-term memory Salp-J Colony Optimization hybrid ensemble deep predictor

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